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Hi, I'm Uday Paripelli, an AI/ML Engineer based in St. Louis with 4+ years of experience delivering production-grade ML and GenAI solutions across finance, healthcare, and enterprise settings. I specialize in building LLM-powered applications, RAG pipelines, fraud detection, and personalization systems that drive efficiency gains and measurable business impact. I code in Python and Spark, design scalable cloud and MLOps architectures, and focus on responsible AI, model observability, and explainability. I enjoy collaborating with cross-functional teams to align AI solutions with regulatory and governance requirements.…Hi, I'm Uday Paripelli, an AI/ML Engineer based in St. Louis with 4+ years of experience delivering production-grade ML and GenAI solutions across finance, healthcare, and enterprise settings. I specialize in building LLM-powered applications, RAG pipelines, fraud detection, and personalization systems that drive efficiency gains and measurable business impact. I code in Python and Spark, design scalable cloud and MLOps architectures, and focus on responsible AI, model observability, and explainability. I enjoy collaborating with cross-functional teams to align AI solutions with regulatory and governance requirements.

Uday Paripelli

Data Scientist, Developer, AI Prompt Engineer, +6

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Hi, I'm Uday Paripelli, an AI/ML Engineer based in St. Louis with 4+ years of experience delivering production-grade ML and GenAI solutions across finance, healthcare, and enterprise settings. I specialize in building LLM-powered applications, RAG pipelines, fraud detection, and personalization systems that drive efficiency gains and measurable business impact. I code in Python and Spark, design scalable cloud and MLOps architectures, and focus on responsible AI, model observability, and explainability. I enjoy collaborating with cross-functional teams to align AI solutions with regulatory and governance requirements.…Hi, I'm Uday Paripelli, an AI/ML Engineer based in St. Louis with 4+ years of experience delivering production-grade ML and GenAI solutions across finance, healthcare, and enterprise settings. I specialize in building LLM-powered applications, RAG pipelines, fraud detection, and personalization systems that drive efficiency gains and measurable business impact. I code in Python and Spark, design scalable cloud and MLOps architectures, and focus on responsible AI, model observability, and explainability. I enjoy collaborating with cross-functional teams to align AI solutions with regulatory and governance requirements.

Available to hire

Hi, I’m Uday Paripelli, an AI/ML Engineer based in St. Louis with 4+ years of experience delivering production-grade ML and GenAI solutions across finance, healthcare, and enterprise settings. I specialize in building LLM-powered applications, RAG pipelines, fraud detection, and personalization systems that drive efficiency gains and measurable business impact.

I code in Python and Spark, design scalable cloud and MLOps architectures, and focus on responsible AI, model observability, and explainability. I enjoy collaborating with cross-functional teams to align AI solutions with regulatory and governance requirements.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

AI/ML Engineer at Morgan Stanley

November 1, 2024 - November 27, 2025

Architected and deployed LLM-powered agentic systems using LangChain, LangGraph, and CrewAI to enable inter-agent memory and collaboration, improving workflow task completion by 30% across multiple financial operations. Fine-tuned LLMs (Hugging Face, LangChain) for clinical and legal summarization tasks, increasing accuracy by 27% and reducing analyst review workload by ~15 hours per week. Deployed Generative AI solutions via AWS Bedrock (Claude, GPT-4) for document summarization and regulatory filings, achieving ~45% faster processing with scalable automation. Optimized SageMaker lifecycle configurations and right-sizing strategies, cutting compute costs by ~18% (~$200K annually). Designed and implemented RAG pipelines with Pinecone and FAISS, improving document retrieval accuracy by ~35% and saving ~$400K yearly. Built a real-time fraud detection pipeline using XGBoost, PySpark, and SageMaker, achieving sub-second predictions, 42% higher detection accuracy, and 30% fewer false positi

ML Engineer at Groovy Web

July 1, 2023 - July 1, 2023

Conducted A/B testing of LLM prompts using LangChain and evaluation metrics (BLEU, ROUGE), improving chatbot response accuracy by 18% and customer satisfaction scores. Built deep learning models (CNN, RNN, LSTM, Transformers) for anomaly detection and predictive maintenance, achieving >90% accuracy and reducing MTTR by 25%. Automated ETL pipelines using Python, SQL, and SAS to streamline model ingestion, improving data throughput by 60% and reducing preparation time from days to hours. Developed scalable ML pipelines using PySpark, AWS Glue, and Redshift for real-time analytics, improving data delivery speed and business insights by 60%. Designed XGBoost-based failure prediction models that reduced system downtime by 15% and prevented ~$1.2M in annual revenue loss. Implemented MLOps pipelines with Docker, Kubernetes, and MLflow to deploy and monitor financial models, achieving 99.9% uptime and reducing model release cycles by 35%. Built interactive dashboards and EDA visualizations (Ma

ML Engineer at Groovy Web

May 1, 2020 - July 1, 2023

Built deep learning models for anomaly detection and predictive maintenance achieving >90% accuracy; automated ETL pipelines; implemented MLOps pipelines with Docker, Kubernetes, and MLflow to deploy and monitor models with 99.9% uptime; led data analytics and dashboards.